Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Ronnie Concepcion II , Bernardo Duarte , Maria Gemel Palconit , Jonah Jahara Baun , Argel Bandala , Ryan Rhay Vicerra , Elmer Dadios
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引用次数: 0

Abstract

Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds, fish effluents, and degrading biomaterials on the aquaponic pond floor, and when aquacultural malpractices occur, large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture plants. Recent nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive materials. In response to the challenge of sensor enhancement and aquaponic water quality monitoring, this study developed sensitive, repeatable, and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces, integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond water. Five SPE electrical traces were designed on the two types of substrates. Scanning electron microscopy with energy dispersive X-ray confirmed the electrode surface morphology. Electrochemical cyclic voltammetry using 10 to 100 mg/L KNO3 and water from three-depth regions of the actual pond established the electrochemical test time (10.5 s) and electrode potential (0.135 V) protocol necessary to produce peak current that corresponds to the strength of nitrate ions during redox. The findings from in situ testing revealed that the proposed sensors have strong linear predictions (R2 = 0.968 MSE = 1.659 for nSPEv and R2 = 0.966 MSE = 4.697 for nSPEp) in the range of 10 to 100 mg/L and best detection limit of 3.15 μg/L, which are comparable to other sensors of more complex construction. The developed three-electrode electrochemical nitrate sensor confirms that it is reliable for both biosensing in controlled solutions and in situ aquaponic pond water systems.

Abstract Image

聚氯乙烯和羊皮纸带丝网印刷石墨电极与遗传程序集成用于水培池塘水的原位硝酸盐传感
硝酸盐是植物生长所必需的主要水溶性宏量营养元素,由过量的鱼饲料、鱼类排泄物和水产养殖池塘底层降解的生物材料转化而来,当发生水产养殖不当时,大量硝酸盐会滞留在水系统中,导致富营养化率上升,并使鱼类和水产养殖植物中毒。最近的硝酸盐传感器原型仍需要对水样进行额外的去离子和稀释步骤,而且材料昂贵。为了应对传感器改进和水产养殖水质监测方面的挑战,本研究在聚氯乙烯和羊皮纸基底上开发了灵敏、可重复、可再现的丝网印刷石墨电极,电极材料为银,石墨粉的比例为 60:40:电痕采用指甲油配制的导电墨水,并集成了 9 个基因遗传表达模型,该模型是硝酸盐浓度预测的阳极峰值电流和电化学测试时间的函数,嵌入到低功耗 Arduino ESP32 中,用于水产养殖池塘水中硝酸盐的原位传感。在两种基底上设计了五种 SPE 电迹。扫描电子显微镜与能量色散 X 射线证实了电极表面形态。使用 10 至 100 mg/L KNO3 和来自实际池塘三个深度区域的水进行电化学循环伏安测试,确定了产生峰值电流所需的电化学测试时间(10.5 秒)和电极电位(0.135 V)协议,该峰值电流与氧化还原过程中硝酸根离子的强度相对应。原位测试结果表明,所提出的传感器在 10 至 100 mg/L 范围内具有很强的线性预测能力(nSPEv 的 R2 = 0.968 MSE = 1.659,nSPEp 的 R2 = 0.966 MSE = 4.697),最佳检测限为 3.15 μg/L,可与其他结构更复杂的传感器相媲美。所开发的三电极电化学硝酸盐传感器证实了其在受控溶液中的生物传感和原位水产养殖池塘水系统中的可靠性。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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